Systems and methods to determine media effectiveness

ABSTRACT

Example systems and methods to determine media effectiveness are disclosed. An example system includes a synchronizer to time shift first neuro-response data gathered from an audience member exposed to media a first amount to align the first neuro-response data with second neuro-response data simultaneously gathered from the audience member to form aligned data, the first neuro-response data representing a first response to a first sensory component of the media and the second neuro-response data representing a second response to a second sensory component of media. The synchronizer is to time shift the second neuro-response data a second amount. The first amount is based on a first cognitive delay of a brain of the audience member and the second amount is based on a second cognitive delay of the brain. The example system includes an analyzer to determine an effectiveness of the media based on the aligned data.

RELATED APPLICATIONS

This patent arises from a continuation of U.S. patent application Ser.No. 14/673,077, filed on Mar. 30, 2015, U.S. patent application Ser. No.13/659,592, filed on Oct. 24, 2012, now U.S. Pat. No. 9,021,515, U.S.patent application Ser. No. 12/244,751, filed on Oct. 2, 2008, now U.S.Pat. No. 8,327,395, and U.S. patent application Ser. No. 12/244,752,filed on Oct. 2, 2008, now U.S. Pat. No. 8,332,883, which are herebyincorporated by reference in their entireties.

This patent claims the benefit of U.S. Patent Application Ser. No.60/977,035, filed Oct. 2, 2007.

This patent claims the benefit of U.S. Patent Application Ser. No.60/977,040, filed Oct. 2, 2007.

This patent claims the benefit of U.S. Patent Application Ser. No.60/977,042, filed Oct. 2, 2007.

This patent claims the benefit of U.S. Patent Application Ser. No.60/977,045, filed Oct. 2, 2007.

This patent claims the benefit of U.S. Patent Application Ser. No.60/984,260, filed Oct. 31, 2007.

This patent claims the benefit of U.S. Patent Application Ser. No.60/984,268, filed Oct. 31, 2007.

This patent claims the benefit of U.S. Patent Application Ser. No.60/991,591, filed Nov. 30, 2007.

This patent is related to U.S. patent application Ser. No. 11/681,265,filed Mar. 2, 2007; U.S. patent application Ser. No. 11/804,517, filedMay 17, 2007; U.S. patent application Ser. No. 11/779,814, filed Jul.18, 2007; U.S. patent application Ser. No. 11/846,068, filed Aug. 28,2007; U.S. patent application Ser. No. 11/959,399, filed Dec. 18, 2007;U.S. patent application Ser. No. 12/244,737, filed Oct. 2, 2008; U.S.patent application Ser. No. 12/244,748, filed Oct. 2, 2008; U.S. patentapplication Ser. No. 12/263,331, filed Oct. 31, 2008; U.S. patentapplication Ser. No. 12/263,350, filed Oct. 31, 2008; U.S. patentapplication Ser. No. 12/326,016, filed Dec. 1, 2008; and U.S. patentapplication Ser. No. 13/252,910, filed Oct. 4, 2011.

TECHNICAL FIELD

This disclosure relates to the field of analysis of physiologicalresponses from viewers of media instances.

BACKGROUND

A key to creating a high performing media instance is to ensure thatevery event in the media elicits the desired responses from viewers.Here, the media instance can be but is not limited to, a video game, anadvertisement clip, a movie, a computer application, a printed media(e.g., a magazine), a website, an online advertisement, a recordedvideo, a live performance of media, and other types of media.

Physiological data, which includes but is not limited to heart rate,brain waves, electroencephalogram (EEG) signals, blink rate, breathing,motion, muscle movement, galvanic skin response and any other responsecorrelated with changes in emotion of a viewer of a media instance, cangive a trace (e.g., a line drawn by a recording instrument) of theviewer's responses while he/she is watching the media instance. Thephysiological data can be measure by one or more physiological sensors,each of which can be but is not limited to, an electroencephalogram,electrocardiogram, an accelerometer, a blood oxygen sensor, agalvanometer, an electromyograph, skin temperature sensor, breathingsensor, eye tracking, pupil dilation sensing, and any otherphysiological sensor.

It is well established that physiological data in the human body of aviewer correlates with the viewer's change in emotions. Thus, from themeasured “low level” physiological data, “high level” (e.g., easier tounderstand, intuitive to look at) physiological responses from theviewers of the media instance can be created. An effective mediainstance that connects with its audience/viewers is able to elicit thedesired emotional response. Here, the high level physiological responsesinclude, but are not limited to, liking (valence)—positive/negativeresponses to events in the media instance, intent to purchase or recall,emotional engagement in the media instance, thinking—amount of thoughtsand/or immersion in the experience of the media instance, andadrenaline—anger, distraction, frustration, and other emotionalexperiences to events in the media instance, and tension and stress.

Advertisers, media producers, educators, scientists, engineers, doctorsand other relevant parties have long desired to have greater access tocollected reactions to their media products and records of responsesthrough a day from their targets, customers, clients and pupils. Theseparties desire to understand the responses people have to theirparticular stimulus in order to tailor their information or mediainstances to better suit the needs of end users and/or to increase theeffectiveness of the media instance created. Making the reactions to themedia instances available remotely over the Web to these interestedparties has potentially very large commercial and socially positiveimpacts. Consequently, allowing a user to remotely access and analyzethe media instance and the physiological responses from numerous viewersto the media instance is desired.

INCORPORATION BY REFERENCE

Each patent, patent application, and/or publication mentioned in thisspecification is herein incorporated by reference in its entirety to thesame extent as if each individual patent, patent application, and/orpublication was specifically and individually indicated to beincorporated by reference. Notwithstanding the prior sentence, U.S.patent application Ser. No. 12/244,737, filed Oct. 2, 2008; U.S. patentapplication Ser. No. 12/244,748, filed Oct. 2, 2008; U.S. patentapplication Ser. No. 12/263,331, filed Oct. 31, 2008; U.S. patentapplication Ser. No. 12/244,752, filed Oct. 2, 2008; U.S. patentapplication Ser. No. 12/263,350, filed Oct. 31, 2008; U.S. patentapplication Ser. No. 12/326,016, filed Dec. 1, 2008; and U.S. patentapplication Ser. No. 13/252,910, filed Oct. 4, 2011 are not incorporatedby reference.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of an exemplary system to support remoteaccess and analysis of media and reactions from viewers.

FIG. 2 is a flow chart illustrating an exemplary process to supportremote access and analysis of media and reactions from viewers.

FIG. 3 shows one or more exemplary physiological responses aggregatedfrom the viewers and presented in the response panel of the interactivebrowser.

FIG. 4 shows exemplary verbatim comments and feedbacks collected fromthe viewers and presented in the response panel of the interactivebrowser.

FIG. 5 shows exemplary answers to one or more survey questions collectedfrom the viewers and presented as a pie chart in the response panel ofthe interactive browser.

FIG. 6 shows exemplary answers to one or more survey questions collectedfrom the viewers and presented as a histogram

FIG. 7 shows an exemplary graph displaying the percentages of viewerswho “liked” or “really liked” a set of advertisements.

FIG. 8 is an illustration of an exemplary system to support providingactionable insights based on in-depth analysis of reactions fromviewers.

FIG. 9 is a flow chart illustrating an exemplary process to supportproviding actionable insights based on in-depth analysis of reactionsfrom viewers.

FIG. 10 shows exemplary highlights and arrows representing trends in thephysiological responses from the viewers as well as verbal explanationof such markings.

FIG. 11 is an illustration of an exemplary system to supportsynchronization of media with physiological responses from viewers.

FIG. 12 is a flow chart illustrating an exemplary process to supportsynchronization of media with physiological responses from viewers.

FIG. 13 is an illustration of an exemplary system to support graphicalpresentation of verbatim comments from viewers.

FIG. 14 is a flow chart illustrating an exemplary process to supportgraphical presentation of verbatim comments from viewers.

FIG. 15 shows an exemplary bubble graph presenting summation of positiveand negative comments from the viewers to various aspects of the mediainstance.

FIG. 16 shows an exemplary word cloud presenting key words and conceptsfrom the viewers of the media instance.

DETAILED DESCRIPTION

Examples disclosed herein enable remote and interactive access,navigation, and analysis of reactions from one or more viewers to aspecific media instance. Here, the reactions include, but are notlimited to, physiological responses, survey results, verbatim feedback,event-based metadata, and derived statistics for indicators of successand failure from the viewers. The reactions from the viewers areaggregated and stored in a database and are delivered to a user via aweb-based graphical interface or application, such as a Web browser.Through the web-based graphical interface, the user is able to remotelyaccess and navigate the specific media instance, together with one ormore of: the aggregated physiological responses that have beensynchronized with the media instance, the survey results, and theverbatim feedbacks related to the specific media instance. Instead ofbeing presented with static data (such as a snapshot) of the viewers'reactions to the media instance, the user is now able to interactivelydivide, dissect, parse, and analysis the reactions in any way he/sheprefer. The examples disclosed herein provide automation that enablesthose who are not experts in the field of physiological analysis tounderstand and use physiological data by enabling these non-experts toorganize the data and organize and improve presentation or visualizationof the data according to their specific needs. In this manner, theexamples disclosed herein provide an automated process that enablesnon-experts to understand complex data, and to organize the complex datain such a way as to present conclusions as appropriate to the mediainstance.

In the following description, numerous specific details are introducedto provide a thorough understanding of, and enabling description for,example systems and methods. One skilled in the relevant art, however,will recognize that these examples can be practiced without one or moreof the specific details, or with other components, systems, etc. Inother instances, well-known structures or operations are not shown, orare not described in detail, to avoid obscuring aspects of the disclosedexamples.

Having multiple reactions from the viewers (e.g., physiologicalresponses, survey results, verbatim feedback, events tagged withmetadata, etc.) available in one place and at a user's fingertips, alongwith the automated methods for aggregating the data provided herein,allows the user to view the reactions to hundreds of media instances inone sitting by navigating through them. For each of the media instances,the integration of multiple reactions provides the user with moreinformation than the sum of each of the reactions to the media instance.For a non-limiting example, if one survey says that an ad is bad, thatis just information; but if independent surveys, verbatim feedbacks andphysiological data across multiple viewers say the same, the reactionsto the media instance become more trustworthy. By combining this beforea user sees it, the correct result is presented to the user.

FIG. 1 is an illustration of an example system to support automatedremote access and analysis of media and reactions from viewers. Althoughthis diagram depicts components as functionally separate, such depictionis merely for illustrative purposes. It will be apparent to thoseskilled in the art that the components portrayed in this figure can bearbitrarily combined or divided into separate software, firmware and/orhardware components. Furthermore, it will also be apparent to thoseskilled in the art that such components, regardless of how they arecombined or divided, can execute on the same computing device ormultiple computing devices, and wherein the multiple computing devicescan be connected by one or more networks.

Referring to FIG. 1, an authentication module 102 is operable toauthenticate identity of a user 101 requesting access to a mediainstance 103 together with one or more reactions 104 from a plurality ofviewers of the media instance remotely over a network 107. Here, themedia instance and its pertinent data can be stored in a media database105, and the one or more reactions from the viewers can be stored in areaction database 106, respectively. The network 107 can be, but is notlimited to, one or more of the internet, intranet, wide area network(WAN), local area network (LAN), wireless network, Bluetooth, and mobilecommunication networks. Once the user is authenticated, a presentationmodule 108 is operable to retrieve and present the requested information(e.g., the media instance together with one or more reactions from theplurality of viewers) to the user via an interactive browser 109. Theinteractive browser 109 comprises at least two panels including a mediapanel 110, which is operable to present, play, and pause the mediainstance, and a response panel 111, which is operable to display the oneor more reactions corresponding to the media instance, and provide theuser with a plurality of features to interactively divide, dissect,parse, and analyze the reactions.

FIG. 2 is a flow chart illustrating an exemplary process to supportremote access and analysis of media and reactions from viewers. Althoughthis figure depicts functional steps in a particular order for purposesof illustration, the process is not limited to any particular order orarrangement of steps. One skilled in the art will appreciate that thevarious steps portrayed in this figure could be omitted, rearranged,combined and/or adapted in various ways.

Referring to FIG. 2, a media instance and one or more reactions to theinstance from a plurality of viewers are stored and managed in one ormore databases at step 201. Data or information of the reactions to themedia instance is obtained or gathered from each user via a sensorheadset, one example of which is described in U.S. patent applicationSer. No. 12/206,676, filed Sep. 8, 2008, U.S. patent application Ser.No. 11/804,517, filed May 17, 2007, and U.S. patent application Ser. No.11/681,265, filed Mar. 2, 2007. At step 202, the identity of a userrequesting access to the media instance and the one or more reactionsremotely is authenticated. At step 203, the requested media instance andthe one or more reactions are retrieved and delivered to the userremotely over a network (e.g., the Web). At step 204, the user mayinteractively aggregate, divide, dissect, parse, and analyze the one ormore reactions to draw conclusions about the media instance.

In some examples, alternative forms of access to the one or morereactions from the viewers other than over the network may be adopted.For non-limiting examples, the reactions can be made available to theuser on a local server on a computer or on a recordable media such as aDVD disc with all the information on the media.

In some examples, with reference to FIG. 1, an optional analysis module112 is operable to perform in-depth analysis on the viewers' reactionsto a media instance as well as the media instance itself (e.g.,dissecting the media instance into multiple scenes/events/sections).Such analysis provides the user with information on how the mediainstance created by the user is perceived by the viewers. In addition,the analysis module is also operable to categorize viewers' reactionsinto the plurality of categories.

In some examples, user database 113 stores information of users who areallowed to access the media instances and the reactions from theviewers, and the specific media instances and the reactions each user isallowed to access. The access module 106 may add or remove a user foraccess, and limit or expand the list of media instances and/or reactionsthe user can access and/or the analysis features the user can use bychecking the user's login name and password. Suchauthorization/limitation on a user's access can be determined based uponwho the user is, e.g., different amounts of information for differenttypes of users. For a non-limiting example, Company ABC can have accessto certain ads and survey results of viewers' reactions to the ads,which Company XYZ cannot or have only limited access to.

In some examples, one or more physiological responses aggregated fromthe viewers can be presented in the response panel 111 as lines ortraces 301 in a two-dimensional graph or plot as shown in FIG. 3.Horizontal axis 302 of the graph represents time, and vertical axis 303of the graph represents the amplitude (intensity) of the one or morephysiological responses. Here, the one or more physiological responsesare aggregated over the viewers via one or more of: max, min, average,deviation, or a higher ordered approximation of the intensity of thephysiological responses from the viewers. The responses are synchronizedwith the media instance at each and every moment over the entireduration of the media instance, allowing the user to identify thesecond-by second changes in viewers' emotions and their causes. Acutting line 304 marks the physiological responses from the viewerscorresponding to the current scene (event, section, or moment in time)of the media instance. The cutting line moves in coordination with themedia instance being played.

In some examples, change (trend) in amplitude of the aggregatedresponses is also a good measure of the quality of the media instance.If the media instance is able to change viewers emotions up and down ina strong manner (for a non-limiting example, mathematical deviation ofthe response is large), such strong change in amplitude corresponds to agood media instance that puts the viewers into different emotionalstates. In contrast, a poor performing media instance does not put theviewers into different emotional states. The amplitudes and the trend ofthe amplitudes of the responses are good measures of the quality of themedia instance. Such information can be used by media designers toidentify if the media instance is eliciting the desired response andwhich key events/scenes/sections of the media instance need to bechanged in order to match the desired response. A good media instanceshould contain multiple moments/scenes/events that are intense andproduce positive amplitude of response across viewers. A media instancethat failed to create such responses may not achieve what the creatorsof the media instance have intended.

In some examples, other than providing a second by second view for theuser to see how specific events in the media instance affect theviewers' emotions, the aggregated responses collected and calculated canalso be used for the compilation of aggregate statistics, which areuseful in ranking the overall affect of the media instance. Suchstatistics include but are not limited to Average Liking and Heart RateDeviation.

In some examples, the viewers of the media instance are free to writecomments (e.g., what they like, what they dislike, etc.) on the mediainstance, and the verbatim (free flowing text) comments or feedbacks 401from the viewers can be recorded and presented in a response panel 111as shown in FIG. 4. Such comments can be prompted, collected, andrecorded from the viewers while they are watching the specific mediainstance and the most informative ones are put together and presented tothe user. The user may then analyze, and digest keywords in the commentsto obtain a more complete picture of the viewers' reactions. Inaddition, the user can search for specific keywords he/she is interestedin about the media instance, and view only those comments containing thespecified keywords.

In some examples, the viewers' comments about the media instance can becharacterized as positive or negative in a plurality ofcategories/topics/aspects related to the product, wherein suchcategories include but are not limited to, product, event, logo, song,spokesperson, jokes, narrative, key events, storyline. These categoriesmay not be predetermined, but instead be extracted from the analysis oftheir comments.

In some examples, answers to one or more survey questions 501 aggregatedfrom the viewers can be rendered graphically, for example, by beingpresented in the response panel 111 in a graphical format 502 as shownin FIG. 5 Alternatively, FIG. 6 is an exemplary histogram displaying theresponse distribution of viewers asked to rate an advertisement on ascale of 1-5. Here, the graphical format can be but is not limited to, abar graph, a pie chart (e.g., as shown in FIG. 5), a histogram (e.g., asshown in FIG. 6), or any other suitable graph type.

In some examples, the survey questions can be posed or presented to theviewers while they are watching the specific media instance and theiranswers to the questions are collected, recorded, summed up bypre-defined categories via a surveying module 114. Once the surveyresults are made available to the user (creator of the media instance),the user may pick any of the questions, and be automatically presentedwith survey results corresponding to the question visually to the user.The user may then view and analyze how viewers respond to specificquestions to obtain a more complete picture of the viewers' reactions.

In some examples, many different facets of the one or more reactionsfrom the viewers described above can be blended into a few simplemetrics that the user can use to see how it is currently positionedagainst the rest of their industry. For the user, knowing where it ranksin its industry in comparison to its competition is often the first stepin getting to where it wants to be. For a non-limiting example, inaddition to the individual survey results of a specific media instance,the surveying module may also provide the user with a comparison ofsurvey results and statistics to multiple media instances. Thisautomation allows the user not only to see the feedback that the viewersprovided with respect to the specific media instance, but also toevaluate how the specific media instance compares to other mediainstances designed by the same user or its competitors. FIG. 7 shows anexemplary graph displaying the percentages of viewers who “liked” or“really liked” a set of advertisements, which helps to determine if anew ad is in the top quartile with respect to other ads.

Some examples disclosed herein provide a user not only with tools foraccessing and obtaining a maximum amount of information out of reactionsfrom a plurality of viewers to a specific media instance, but also withactionable insights on what changes the user can make to improve themedia instance based on in-depth analysis of the viewers' reactions.Such analysis requires expert knowledge on the viewers' physiologicalbehavior and large amounts of analysis time, which the user may notpossess. Here, the reactions include but are not limited to,physiological responses, survey results, and verbatim feedbacks from theviewers, to name a few. The reactions from the viewers are aggregatedand stored in a database and presented to the user via a graphicalinterface, as described above. In some examples, predefined methods forextracting information from the reactions and presenting thatinformation are provided so that the user is not required to be anexpert in physiological data analysis to reach and understandconclusions supported by the information. Making in-depth analysis ofreactions to media instances and actionable insights available to a userenables a user who is not an expert in analyzing physiological data toobtain critical information that can have significant commercial andsocially positive impacts.

FIG. 8 is an illustration of an exemplary system to support providingactionable insights based on in-depth analysis of reactions fromviewers. Although this diagram depicts components as functionallyseparate, such depiction is merely for illustrative purposes. It will beapparent to those skilled in the art that the components portrayed inthis figure can be arbitrarily combined or divided into separatesoftware, firmware and/or hardware components. Furthermore, it will alsobe apparent to those skilled in the art that such components, regardlessof how they are combined or divided, can execute on the same computingdevice or multiple computing devices, and wherein the multiple computingdevices can be connected by one or more networks.

Referring to FIG. 8, a collection module 803 is operable to collect,record, store and manage one or more reactions 802 from a plurality ofviewers of a media instance 801. The viewers from whom reactions 802 arecollected can be in the same physical location or different physicallocations. Additionally, the viewers can be viewing the media instanceand the reactions collected at the same time, or at different times(e.g., viewer 1 is viewing the media instance at 9 AM while viewer 2 isviewing the media instance at 3 PM). Data or information of thereactions to the media instance is obtained or gathered from each uservia a sensor headset. In some examples, the sensor headset integratessensors into a housing which can be placed on a human head formeasurement of physiological data. The device includes at least onesensor and can include a reference electrode connected to the housing. Aprocessor coupled to the sensor and the reference electrode receivessignals that represent electrical activity in tissue of a user. Theprocessor generates an output signal including data of a differencebetween an energy level in each of a first and second frequency band ofthe signals. The difference between energy levels is proportional torelease level present time emotional state of the user. The headsetincludes a wireless transmitter that transmits the output signal to aremote device. The headset therefore processes the physiological data tocreate the output signal that correspond to a person's mental andemotional state (reactions or reaction data). An example of a sensorheadset is described in U.S. patent application Ser. No. 12/206,676,filed Sep. 8, 2008, Ser. No. 11/804,517, filed May 17, 2007, and Ser.No. 11/681,265, filed Mar. 2, 2007.

The media instance and its pertinent data can be stored in a mediadatabase 804, and the one or more reactions from the viewers can bestored in a reaction database 805, respectively. An analysis module 806performs in-depth analysis on the viewers' reactions and providesactionable insights on the viewers' reactions to a user 807 so that theuser can draw its own conclusion on how the media instance can/should beimproved. A presentation module 808 is operable to retrieve and presentthe media instance 801 together with the one or more reactions 802 fromthe viewers of the media instance via an interactive browser 809. Here,the interactive browser includes at least two panels—a media panel 810,operable to present, play, and pause the media instance, and a reactionpanel 811, operable to display the one or more reactions correspondingto the media instance as well as the key insights provided by theanalysis module 806.

FIG. 9 is a flow chart illustrating an exemplary automatic process tosupport providing actionable insights based on in-depth analysis ofreactions from viewers. Although this figure depicts functional steps ina particular order for purposes of illustration, the process is notlimited to any particular order or arrangement of steps. One skilled inthe art will appreciate that the various steps portrayed in this figurecould be omitted, rearranged, combined and/or adapted in various ways.

Referring to FIG. 9, one or more reactions to a media instance from aplurality of viewers are collected, stored and managed in one or moredatabases at step 901. At step 902, in-depth analysis is performed onthe viewers' reactions using expert knowledge, and actionable insightsare generated based on the viewers' reactions and provided to a user atstep 903 so that the user can draw its own conclusion on the mediainstance can/should be improved. At step 904, the one or more reactionscan be presented to the user together with the actionable insights toenable the user to draw its own conclusions about the media instance.The configuration used to present the reactions and actionable insightscan be saved and tagged with corresponding information, allowing it tobe recalled and used for similar analysis in the future.

In some examples, the analysis module is operable to provide insights orpresent data based in-depth analysis on the viewers' reactions to themedia instance on at least one question. An example question is whetherthe media instance performs most effectively across all demographicgroups or especially on a specific demographic group, e.g., older women?Another example question is whether certain elements of the mediainstance, such as loud noises, were very effective at engaging viewersin a positive, challenging way? Yet another example question is whetherthought provoking elements in the media instance were much more engagingto viewers than product shots? Also, an example question includeswhether certain characters, such as lead female characters, appearing inthe media instance were effective for male viewers and/or across targetaudiences in the female demographic? Still another example questionincludes whether physiological responses to the media instance from theviewers were consistent with viewers identifying or associatingpositively with the characters in the media instance? A further questionis whether the media instance was universal—performed well at connectingacross gender, age, and income boundaries, or highly polarizing?

The analysis module therefore automates the analysis through use of oneor more questions, as described above. The questions provide a contextfor analyzing and presenting the data or information received fromviewers in response to the media instance. The analysis module isconfigured, using the received data, to answer some number of questions,where answers to the questions provide or correspond to the collecteddata. When a user desires results from the data for a particular mediainstance, the user selects a question to which they desire an answer forthe media instance. In response to the question selection, the resultsof the analysis are presented in the form of an answer to the question,where the answer is derived or generated using the data collected andcorresponding to the media instance. The results of the analysis can bepresented using textual and/or graphical outputs or presentations. Theresults of the analysis can also be generated and presented usingprevious knowledge of how to represent the data to answer the question,the previous knowledge coming from similar data analyzed in the past.Furthermore, presentation of data of the media instance can be modifiedby the user through user or generation of other questions.

The analysis module performs the operations described above inconjunction with the presentation module, where the presentation moduleincludes numerous different renderings for data. In operation, arendering is specified or selected for a portion of data of a mediainstance, and the rendering is then tagged with one or more questionsthat apply to the data. This architecture allows users to modify howdata is represented using a set of tools. The system remembers or storesinformation of how data was represented and the question or questiontype that was being answered. This information of prior systemconfigurations allows the system, at a subsequent time, toself-configure to answer the same or similar questions for the samemedia instance or for different media instances. Users thus continuallyimprove the ability of the system to answer questions and improve thequality of data provided in the answers.

In some examples, the presentation module is operable to enable the userto pick a certain section 1001 of the reactions to the media instance1002, such as the physiological responses 1003 from the viewers shown inthe reaction panel 1011 via, for a non-limiting example, “shading”, asshown in FIG. 10. The analysis module 1006 may then perform the analysisrequested on the shaded section of media instance and/or physiologicalresponses automatically to illustrate the responses in a way that a layperson can take advantage of expert knowledge in parsing the viewers'reaction. The analyzed results can then be presented to the user in realtime and can be shared with other people.

In some examples, the analysis module is operable to analyze the shadedsection of the media instance and/or responses by being preprogrammedeither by an analyst or the user themselves. Usually, a user is mostoften interested in a certain number of attributes of the viewers'responses. The analysis module provides the user with insights,conclusions, and findings that they can review from the bottom up.Although the analysis result provides inside and in-depth analysis ofthe data as well as various possible interpretations of the shadedsection of the media instance, which often leaves a conclusion evident,such analysis, however, is no substitute for reaching conclusion by theuser Instead the user is left to draw his/her own conclusion about thesection based on the analysis provided.

In some examples, a user may pick a section and choose one of thequestions/tasks/requests 1004 that he/she is interested in from aprepared list. The prepared list of questions may include but is notlimited to any number of questions. Some example questions follow alongwith a response evoked in the analysis module.

An example question is “Where were there intense responses to the mediainstance?” In response the analysis module may calculate the intensityof the responses automatically by looking for high coherence areas ofresponses.

Another example question is “Does the media instance end on a happynote?” or “Does the audience think the event (e.g., joke) is funny?” Inresponse the analysis module may check if the physiological data showsthat viewer acceptance or approval is higher in the end than at thebeginning of the media instance.

Yet another example question is “Where do people engage in the spot?” Inresponse to this question the analysis module may check if there is acoherent change in viewers' emotions.

Still another example question is “What is the response to the brandmoment?” In response the analysis module may check if thought goes up,but acceptance or approval goes down during the shaded section of themedia.

An additional example question is “Which audience does the productintroduction work on best?” In response the analysis module analyzes theresponses from various segments of the viewers, which include but arenot limited to, males, females, gamers, republicans, engagement relativeto an industry, etc.

In some examples, the presentation module (FIG. 8, 807) is operable topresent the analysis results in response to the questions raisedtogether with the viewers' reactions to the user graphically on theinteractive browser. For non-limiting examples, line highlights 1005 andarrows 1006 representing trends in the physiological responses from theviewers can be utilized as shown in FIG. 10, where highlights mark oneor more specific physiological responses (e.g., thought in FIG. 10) tobe analyzed and the up/down arrows indicate rise/fall in thecorresponding responses. In addition, other graphic markings can also beused, which can be but are not limited to, text boxes, viewing data frommultiple groups at once (comparing men to women) and any graphic toolsthat are commonly used to mark anything important. For anothernon-limiting example, a star, dot and/or other graphic element may beused to mark the point where there is the first coherent change and acircle may be used to mark the one with the strongest response.

In some examples, verbal explanation 1007 of the analysis results inresponse to the questions raised can be provided to the user togetherwith graphical markings shown in FIG. 10. Such verbal explanationdescribes the graphical markings (e.g., why an arrow rises, detailsabout the arrow, etc.). For the non-limiting example of an advertisementvideo clip shown in FIG. 10, verbal explanation 1007 states that“Thought follows a very regular sinusoidal pattern throughout thisadvertisement. This is often a result of tension-resolution cycles thatare used to engage viewers by putting them in situations where they areforced to think intensely about what they are seeing and then rewardingthem with the resolution of the situation.” For another non-limitingexample of a joke about a man hit by a thrown rock, the verbalexplanation may resemble something like: “The falling of the man afterbeing hit by a rock creates the initial coherent, positive response inliking. This shows that the actual rock throw is not funny, but the arcthat the person's body takes is. After the body hits the ground, theresponse reverts to neutral and there are no further changes in emotionsduring this section.”

In some examples, with reference to FIG. 8, an optional authenticationmodule 813 is operable to authenticate identity of the user requestingaccess to the media instance and the verbatim reactions remotely over anetwork 812. Here, the network can be but is not limited to, internet,intranet, wide area network (WAN), local area network (LAN), wirelessnetwork, Bluetooth, and mobile communication network.

In some examples, optional user database 814 stores information of userswho are allowed to access the media instances and the verbatim reactionsfrom the viewers, and the specific media instances and the reactionseach user is allowed to access. The access module 810 may add or removea user for access, and limit or expand the list of media instancesand/or reactions the user can access and/or the analysis features theuser can use by checking the user's login name and password. Suchauthorization/limitation on a user's access can be determined to basedupon who the user is, e.g., different amounts of information fordifferent types of users. For a non-limiting example, Company ABC canhave access to certain ads and feedbacks from viewers' reactions to theads, to which Company XYZ cannot have access or can have only limitedaccess.

In some examples, a specific media instance is synchronized withphysiological responses to the media instance from a plurality ofviewers continuously over the entire time duration of the mediainstance. Once the media instance and the physiological responses aresynchronized, an interactive browser enables a user to navigate throughthe media instance (or the physiological responses) in one panel whilepresenting the corresponding physiological responses (or the section ofthe media instance) at the same point in time in another panel.

The interactive browser allows the user to select a section/scene fromthe media instance, correlate, present, and compare the viewers'physiological responses to the particular section. Alternatively, theuser may monitor the viewers' physiological responses continuously asthe media instance is being displayed. Being able to see the continuous(instead of static snapshot of) changes in physiological responses andthe media instance side by side and compare aggregated physiologicalresponses from the viewers to a specific event of the media instance inan interactive way enables the user to obtain better understanding ofthe true reaction from the viewers to whatever stimuli being presentedto them.

FIG. 11 is an illustration of an exemplary system to supportsynchronization of media with physiological responses from viewers ofthe media. Although this diagram depicts components as functionallyseparate, such depiction is merely for illustrative purposes. It will beapparent to those skilled in the art that the components portrayed inthis figure can be arbitrarily combined or divided into separatesoftware, firmware and/or hardware components. Furthermore, it will alsobe apparent to those skilled in the art that such components, regardlessof how they are combined or divided, can execute on the same computingdevice or multiple computing devices, and wherein the multiple computingdevices can be connected by one or more networks.

Referring to FIG. 11, a synchronization module 1103 is operable tosynchronize and correlate a media instance 1101 with one or morephysiological responses 1102 aggregated from one or more viewers of themedia instance continuously at each and every moment over the entireduration of the media instance. Here, the media instance and itspertinent data can be stored in a media database 1104, and the one ormore physiological responses aggregated from the viewers can be storedin a reaction database 1105, respectively. An interactive browser 1106comprises at least two panels including a media panel 1107, which isoperable to present, play, and pause the media instance, and a reactionpanel 1108, which is operable to display and compare the one or morephysiological responses (e.g., Adrenaline, Liking, and Thought)corresponding to the media instance as lines (traces) in atwo-dimensional line graph. A horizontal axis of the graph representstime, and a vertical axis represents the amplitude (intensity) of theone or more physiological responses. A cutting line 1109 marks thephysiological responses from the viewers to the current scene (event,section, or moment in time) of the media instance, wherein the cuttingline can be chosen by the user and move in coordination with the mediainstance being played. The interactive browser enables the user toselect an event/section/scene/moment from the media instance presentedin the media panel 1107 and correlate, present, and compare the viewers'physiological responses to the particular section in the reaction panel1108. Conversely, interactive browser also enables the user to selectthe cutting line 1109 of physiological responses from the viewers in thereaction panel 1108 at any specific moment, and the corresponding mediasection or scene can be identified and presented in the media panel1107.

In some examples, the synchronization module 1103 synchronizes andcorrelates a media instance 1101 with one or more physiologicalresponses 1102 aggregated from a plurality of viewers of the mediainstance by synchronizing each event of the media. The physiologicalresponse data of a person includes but is not limited to heart rate,brain waves, electroencephalogram (EEG) signals, blink rate, breathing,motion, muscle movement, galvanic skin response, skin temperature, andany other physiological response of the person. The physiologicalresponse data corresponding to each event or point in time is thenretrieved from the media database 1104. The data is offset to accountfor cognitive delays in the human brain corresponding to the signalcollected (e.g., the cognitive delay of the brain associated with humanvision is different than the cognitive delay associated with auditoryinformation) and processing delays of the system, and then synchronizedwith the media instance 1101. Optionally, an additional offset may beapplied to the physiological response data 1102 of each individual toaccount for time zone differences between the viewer and reactiondatabase 1105.

FIG. 12 is a flow chart illustrating an exemplary process to supportsynchronization of media with physiological responses from viewers ofthe media. Although this figure depicts functional steps in a particularorder for purposes of illustration, the process is not limited to anyparticular order or arrangement of steps. One skilled in the art willappreciate that the various steps portrayed in this figure could beomitted, rearranged, combined and/or adapted in various ways.

Referring to FIG. 12, a media instance is synchronized with one or morephysiological responses aggregated from a plurality of viewers of themedia instance continuously at each and every moment over the entireduration of the media instance at step 1201. At step 1202, thesynchronized media instance and the one or more physiological responsesfrom the viewers are presented side-by-side. Anevent/section/scene/moment from the media instance can be selected atstep 1203, and the viewers' physiological responses to the particularsection can be correlated, presented, and compared at step 1204.Alternatively, the viewers' physiological responses can be monitoredcontinuously as the media instance is being displayed at step 1205.

In some examples, with reference to FIG. 11, an aggregation module 1110is operable to retrieve from the reaction database 1105 and aggregatethe physiological responses to the media instance across the pluralityof viewers and present each of the aggregated responses as a functionover the duration of the media instance. The aggregated responses to themedia instance can be calculated via one or more of: max, min, average,deviation, or a higher ordered approximation of the intensity of thephysiological responses from the viewers.

In some examples, change (trend) in amplitude of the aggregatedresponses is a good measure of the quality of the media instance. If themedia instance is able to change viewers emotions up and down in astrong manner (for a non-limiting example, mathematical deviation of theresponse is large), such strong change in amplitude corresponds to agood media instance that puts the viewers into different emotionalstates. In contrast, a poor performing media instance does not put theviewers into different emotional states. Such information can be used bymedia designers to identify if the media instance is eliciting thedesired response and which key events/scenes/sections of the mediainstance need to be changed in order to match the desired response. Agood media instance should contain multiple moments/scenes/events thatare intense and produce positive amplitude of response across viewers. Amedia instance failed to create such responses may not achieve what thecreators of the media instance have intended.

In some examples, the media instance can be divided up into instances ofkey moments/events/scenes/segments/sections in the profile, wherein suchkey events can be identified and/tagged according to the type of themedia instance. In the case of video games, such key events include butare not limited to, elements of a video game such as levels, cut scenes,major fights, battles, conversations, etc. In the case of Web sites,such key events include but are not limited to, progression of Webpages, key parts of a Web page, advertisements shown, content, textualcontent, video, animations, etc. In the case of an interactivemedia/movie/ads, such key events can be but are not limited to,chapters, scenes, scene types, character actions, events (fornon-limiting examples, car chases, explosions, kisses, deaths, jokes)and key characters in the movie.

In some examples, an event module 1111 can be used to quickly identify anumbers of moments/events/scenes/segments/sections in the media instanceretrieved from the media database 1104 and then automatically calculatethe length of each event. The event module may enable each user, or atrained administrator, to identify and tag the important events in themedia instance so that, once the “location” (current event) in the mediainstance (relative to other pertinent events in the media instance) isselected by the user, the selected event may be better correlated withthe aggregated responses from the viewers.

In some examples, the events in the media instance can be identified,automatically if possible, through one or more applications that parseuser actions in an environment (e.g., virtual environment, realenvironment, online environment, etc.) either before the viewer'sinteraction with the media instance in the case of non-interactive mediasuch as a movie, or afterwards by reviewing the viewer's interactionwith the media instance through recorded video, a log of actions orother means. In video games, web sites and other electronic interactivemedia instance, the program that administers the media can create thislog and thus automate the process.

An example enables graphical presentation and analysis of verbatimcomments and feedbacks from a plurality of viewers to a specific mediainstance. These verbatim comments are first collected from the viewersand stored in a database before being analyzed and categorized intovarious categories. Once categorized, the comments can then be presentedto a user in various graphical formats, allowing the user to obtain anintuitive visual impression of the positive/negative reactions to and/orthe most impressive characteristics of the specific media instance asperceived by the viewers.

An example enables graphical presentation and analysis of verbatimcomments and feedbacks from a plurality of viewers to a specific mediainstance. These verbatim comments are first collected from the viewersand stored in a database before being analyzed and categorized intovarious categories. Once categorized, the comments can then be presentedto a user in various graphical formats, allowing the user to obtain anintuitive visual impression of the positive/negative reactions to and/orthe most impressive characteristics of the specific media instance, asperceived by the viewers. Instead of parsing through and dissecting thecomments and feedbacks word by word, the user is now able to visuallyevaluate how well the media instance is being received by the viewers ata glance.

FIG. 13 is an illustration of an exemplary system to support graphicalpresentation of verbatim comments from viewers. Although this diagramdepicts components as functionally separate, such depiction is merelyfor illustrative purposes. It will be apparent to those skilled in theart that the components portrayed in this figure can be arbitrarilycombined or divided into separate software, firmware and/or hardwarecomponents. Furthermore, it will also be apparent to those skilled inthe art that such components, regardless of how they are combined ordivided, can execute on the same computing device or multiple computingdevices, and wherein the multiple computing devices can be connected byone or more networks.

Referring to FIG. 13, a collection module 1303 is operable to collect,record, store and manage verbatim reactions 1302 (comments andfeedbacks) from a plurality of viewers of a media instance 1301. Here,the media instance and its pertinent data can be stored in a mediadatabase 1304, and the verbatim reactions from the viewers can be storedin a reaction database 1305, respectively. An analysis module 1306 isoperable to analyze the verbatim comments from the viewers andcategorize them into the plurality of categories. A presentation module1307 is operable to retrieve and categorize the verbatim reactions tothe media instance into various categories, and then present theseverbatim reactions to a user 1308 based on their categories in graphicalforms via an interactive browser 1309. The interactive browser includesat least two panels—a media panel 1310, which is operable to present,play, and pause the media instance, and a comments panel 1311, which isoperable to display not only the one or more reactions corresponding tothe media instance, but also one or more graphical categorization andpresentation of the verbatim reactions to provide the user with both averbal and/or a visual perception and interpretation of the feedbacksfrom the viewers.

FIG. 14 is a flow chart illustrating an exemplary process to supportgraphical presentation of verbatim comments from viewers. Although thisfigure depicts functional steps in a particular order for purposes ofillustration, the process is not limited to any particular order orarrangement of steps. One skilled in the art will appreciate that thevarious steps portrayed in this figure could be omitted, rearranged,combined and/or adapted in various ways.

Referring to FIG. 14, verbatim reactions to a media instance from aplurality of viewers are collected, stored and managed at step 1401. Atstep 1402, the collected verbatim reactions are analyzed and categorizedinto various categories. The categorized comments are then retrieved andpresented to a user in graphical forms based on the categories at step1403, enabling the user to visually interpret the reactions from theviewers at step 1404.

In some examples, the viewers of the media instance are free to writewhat they like and don't like about the media instance, and the verbatim(free flowing text) comments or feedbacks 501 from the viewers can berecorded and presented in the comments panel 111 verbatim as shown inFIG. 4 described above. In some examples, the analysis module isoperable to further characterize the comments in each of the pluralityof categories as positive or negative based on the words used in each ofthe comments. Once characterized, the number of positive or negativecomments in each of the categories can be summed up. For a non-limitingexample, comments from viewers on a certain type of events, like combat,can be characterized and summed up as being 40% positive, while 60%negative. Such an approach avoids single verbatim response from bias theresponses from a group of viewers, making it easy for the user tounderstand how viewers would react to every aspect of the mediainstance.

In some examples, the analysis module is operable to characterize theviewers' comments about the media instance as positive or negative in aplurality of categories/topics/aspects related to the product, whereinsuch categories include but are not limited to, product, event, logo,song, spokesperson, jokes, narrative, key events, storyline. Thesecategories may not be predetermined, but instead be extracted from theanalysis of their comments.

In some examples, the presentation module is operable to presentsummation of the viewers' positive and negative comments to variousaspects/topics/events of the media instance to the user (creator of themedia instance) in a bubble graph, as shown in FIG. 15. The verticalaxis 1501 and horizontal axis 1502 of the bubble graph represent thepercentage of positive or negative comments from the viewers about themedia instance, respectively. Each bubble 1503 in the graph representsone of the topics the viewers have commented upon, marked by the name ofthe event and the percentages of the viewers' negative and positivefeedbacks on the event. The size of the bubble represents the number ofviewers commenting on this specific aspect of the media instance, andthe location of the bubble on the graph indicates whether the commentsfrom the viewers are predominantly positive or negative.

In some examples, the verbatim comments from the viewers can beanalyzed, and key words and concepts (adjectives) can be extracted andpresented in a word cloud, as shown in FIG. 16, rendering meaningfulinformation from the verbatim comments more accessible. Every word inthe word cloud is represented by a circle, square, any other commonlyused geometric shape or simply by the word itself as shown in FIG. 16.Each representation is associated with a corresponding weightrepresented using font sizes or other visual clues. For the non-limitingexample in FIG. 16, the size of each word in the word cloud representsthe number of times or percentages of the viewers use the word in theirresponses. This is useful as a means of displaying “popularity” of anadjective that has been democratically ‘voted’ on to describe the mediainstance and where precise results are not desired. Here, the three mostpopular adjectives used to describe the media instance are “fun”,“cool”, and “boring”.

In some examples, the viewers may simply be asked to answer a specificquestion, for example, “What are three adjectives that best describeyour response to this media.” The adjectives in the viewers' responsesto the question can then be collected, categorized, and summed up, andpresented in a Word cloud. Alternatively, the adjectives the viewersused to describe their responses to the media instance may be extractedfrom collected survey data.

In some examples, with reference to FIG. 13, an optional authenticationmodule 1313 is operable to authenticate identity of the user requestingaccess to the media instance and the verbatim reactions remotely over anetwork 1313. Here, the network can be but is not limited to, internet,intranet, wide area network (WAN), local area network (LAN), wirelessnetwork, Bluetooth, and mobile communication network.

In some examples, optional user database 1314 stores information ofusers who are allowed to access the media instances and the verbatimreactions from the viewers, and the specific media instances and thereactions each user is allowed to access. The access module 1310 may addor remove a user for access, and limit or expand the list of mediainstances and/or reactions the user can access and/or the analysisfeatures the user can use by checking the user's login name andpassword. Such authorization/limitation on a user's access can bedetermined to based upon who the user is, e.g., different amounts ofinformation for different types of users. For a non-limiting example,Company ABC can have access to certain ads and feedback from viewers'reactions to the ads, while Company XYZ cannot have access or can onlyhave limited access to the same ads and/or feedback.

Some of the examples described herein include a method comprising:receiving a media instance, the media instance including a plurality ofmedia events; receiving reaction data from a plurality of viewers whilethe plurality of viewers are viewing the media instance; generatingaggregated reaction data by aggregating the reaction data from theplurality of viewers; generating synchronized data by synchronizing theplurality of media events of the media instance with correspondingaggregated reaction data; and providing controlled access to thesynchronized data from a remote device.

The method of a disclosed example comprises providing, via thecontrolled access, remote interactive manipulation of the reaction datasynchronized to corresponding events of the media instance.

The manipulation of a disclosed example includes at least one ofdividing, dissecting, aggregating, parsing, organizing, and analyzingthe reaction data.

The method of a disclosed example comprises providing controlled accessto at least one of the reaction data and aggregated reaction data.

The method of a disclosed example comprises enabling via the controlledaccess interactive analysis of at least one of the media instance andthe synchronized data.

The method of a disclosed example comprises enabling via the controlledaccess interactive analysis of at least one of the reaction data, theaggregated reaction data, and parsed reaction data.

The reaction data of a disclosed example includes at least one ofphysiological responses, survey results, feedback generated by theviewers, metadata, and derived statistics.

The reaction data of a disclosed example includes physiologicalresponses.

The reaction data of a disclosed example includes survey results.

The reaction data of a disclosed example includes feedback generated bythe viewers.

The reaction data of a disclosed example includes metadata, wherein themetadata is event-based metadata.

The reaction data of a disclosed example includes derived statistics,wherein the derived statistics are derived statistics for indicators ofsuccess and failure of the media instance

Receiving the reaction data of a disclosed example comprises receivingthe reaction data from a plurality of sensor devices via wirelesscouplings, wherein each viewer wears a sensor device of the plurality ofsensor devices.

The method of a disclosed example comprises presenting a user interface(UI), wherein the controlled access is made via the UI.

The method of a disclosed example comprises presenting the synchronizeddata using a rendering of a plurality or renderings.

The plurality of renderings of a disclosed example includes text,charts, graphs, histograms, images, and video.

The aggregating of a disclosed example comprises aggregating thereaction data according to at least one of maximums, minimums, averages,deviations, derivatives, amplitudes, and trends of at least oneparameter of the reaction data.

The method of a disclosed example comprises selecting, via thecontrolled access, a portion of the media instance for which at leastone of the synchronized data, the reaction data, the aggregated reactiondata, and parsed reaction data is viewed. The portion of a disclosedexample includes a point in time. The portion of a disclosed exampleincludes a period of time.

The method of a disclosed example comprises automatically analyzing thereaction data.

The method of a disclosed example comprises providing remote access toresults of the analyzing, and presenting the results, the presentingincluding presenting actionable insights corresponding to a portion ofthe media instance via at least one of a plurality of renderings,wherein the actionable insights correspond to emotional reactions of theplurality of viewers.

The analyzing of a disclosed example includes applying expert knowledgeof physiological behavior to the reaction data.

The method of a disclosed example comprises generating a first set ofquestions that represent the results.

The analyzing of a disclosed example includes analyzing the reactiondata in the context of the first set of questions.

The method of a disclosed example comprises selecting at least onerendering of the plurality of renderings.

The method of a disclosed example comprises tagging the selectedrendering with at least one question of the first set of questions.

A user of a disclosed example can modify the presenting of the resultsvia the selecting of at least one rendering of the plurality ofrenderings.

The presenting of a disclosed example includes presenting the resultsvia presentation of the first set of questions.

The method of a disclosed example comprises, in response to the userselecting a question of the first set of questions, presenting an answerto the selected question that includes the actionable insight.

The method of a disclosed example comprises receiving comments from theplurality of viewers in response to the viewing. The comments of adisclosed example are textual comments. The synchronized data of adisclosed example includes the comments.

The method of a disclosed example comprises presenting survey questionsto the plurality of viewers, the survey questions relating to the mediainstance. The method of a disclosed example comprises receiving answersto the survey questions from the plurality of viewers. The answers tothe survey questions of a disclosed example are textual comments. Thesynchronized data of a disclosed example includes the answers to thesurvey questions.

The plurality of viewers of a disclosed example is at a location.

The plurality of viewers of a disclosed example is at a plurality oflocations.

A first set of the plurality of viewers of a disclosed example is at afirst location and a second set of the plurality of viewers is at asecond location different from the first location.

A first set of the plurality of viewers of a disclosed example isviewing the media instance at a first time and a second set of theplurality of viewers is viewing the media instance at a second timedifferent from the first time.

The reaction data of a disclosed example corresponds to electricalactivity in brain tissue of the user.

The reaction data of a disclosed example corresponds to electricalactivity in muscle tissue of the user.

The reaction data of a disclosed example corresponds to electricalactivity in heart tissue of the user.

Examples described herein include a method comprising: receiving a mediainstance; receiving reaction data from a plurality of viewers, thereaction data generated in response to viewing of the media instance andincluding physiological response data; aggregating the reaction datafrom the plurality of viewers; and providing remote access to at leastone of the reaction data and aggregated reaction data, wherein theremote access enables interactive analysis of at least one of the mediainstance, the reaction data, aggregated reaction data, and parsedreaction data.

Examples described herein include a method comprising: receiving a mediainstance; receiving reaction data from a plurality of viewers, thereaction data generated in response to viewing of the media instance andincluding physiological response data; aggregating the reaction datafrom the plurality of viewers; and enabling remote interactive analysisof the media instance and at least one of the reaction data, aggregatedreaction data, and parsed reaction data.

Examples described herein include a method comprising: receiving a mediainstance; receiving reaction data from a plurality of viewers, thereaction data generated in response to viewing of the media instance andincluding physiological response data; and enabling remote interactivemanipulation of the reaction data synchronized to corresponding eventsof the media instance, the manipulation including at least one ofdividing, dissecting, aggregating, parsing, and analyzing the reactiondata.

Examples described herein include a system comprising: a processorcoupled to a database, the database including a media instance andreaction data, the media instance comprising a plurality of mediaevents, the reaction data received from a plurality of viewers viewingthe media instance; a first module coupled to the processor, the firstmodule generating aggregated reaction data by aggregating the reactiondata from the plurality of viewers, the first module generatingsynchronized data by synchronizing the plurality of media events of themedia instance with corresponding aggregated reaction data; and a secondmodule coupled to the processor, the second module comprising aplurality of renderings and a user interface (UI) that providecontrolled access to the synchronized data from a remote device.

The controlled access of a disclosed example is through the UI andincludes remote interactive manipulation of the reaction datasynchronized to corresponding events of the media instance.

The manipulation of a disclosed example includes at least one ofdividing, dissecting, aggregating, parsing, organizing, and analyzingthe reaction data.

The controlled access of a disclosed example includes access to at leastone of the reaction data and aggregated reaction data.

The controlled access of a disclosed example includes interactiveanalysis of at least one of the media instance and the synchronizeddata.

The controlled access of a disclosed example includes interactiveanalysis of at least one of the reaction data, the aggregated reactiondata, and parsed reaction data.

The plurality of renderings of a disclosed example includes text,charts, graphs, histograms, images, and video.

The UI of a disclosed example presents the synchronized data using atleast one rendering of the plurality or renderings.

The UI of a disclosed example allows selection of a portion of the mediainstance for which at least one of the synchronized data, the reactiondata, the aggregated reaction data, and parsed reaction data is viewed.The portion of a disclosed example includes a point in time. The portionof a disclosed example includes a period of time.

The first module of a disclosed example analyzes the reaction data.

The UI of a disclosed example provides remote access to results of theanalysis.

The UI of a disclosed example presents the results using at least onerendering of the plurality of renderings, the results includingactionable insights corresponding to a portion of the media instance.

The actionable insights of a disclosed example correspond to emotionalreactions of the plurality of viewers.

The analyzing of a disclosed example comprises applying expert knowledgeof physiological behavior to the reaction data.

The system of a disclosed example comprises generating a first set ofquestions that represent the results.

The analyzing of a disclosed example includes analyzing the reactiondata in the context of the first set of questions.

The system of a disclosed example comprises selecting at least onerendering of the plurality of renderings.

The system of a disclosed example comprises tagging the selectedrendering with at least one question of the first set of questions.

A user of a disclosed example can modify presentation of the results viathe UI by selecting at least one rendering of the plurality ofrenderings.

The presenting of a disclosed example includes presenting the resultsvia presentation of the first set of questions on the UI.

The system of a disclosed example comprises, in response to the userselecting a question of the first set of questions, presenting via theUI an answer to the selected question that includes the actionableinsight.

The reaction data of a disclosed example includes at least one ofphysiological responses, survey results, feedback generated by theviewers, metadata, and derived statistics.

The reaction data of a disclosed example includes physiologicalresponses.

The reaction data of a disclosed example includes survey results.

The reaction data of a disclosed example includes feedback generated bythe viewers.

The reaction data of a disclosed example includes metadata. The metadataof a disclosed example is event-based metadata.

The reaction data of a disclosed example includes derived statistics.The derived statistics of a disclosed example are derived statistics forindicators of success and failure of the media instance.

The system of a disclosed example comprises a plurality of sensordevices, wherein each viewer wears a sensor device of the plurality ofsensor devices, wherein each sensor device receives the reaction datafrom a corresponding view and transmits the reaction data to at leastone of the first module and the database.

The aggregating of a disclosed example comprises aggregating thereaction data according to at least one of maximums, minimums, averages,deviations, derivatives, amplitudes, and trends of at least oneparameter of the reaction data.

The system of a disclosed example comprises a third module coupled tothe second module, the third module receiving comments from theplurality of viewers in response to the viewing. The comments of adisclosed example are textual comments. The synchronized data of adisclosed example includes the comments.

The system of a disclosed example comprises a third module coupled tothe second module, the third module presenting survey questions to theplurality of viewers via the UI, the survey questions relating to themedia instance.

The third module of a disclosed example receives answers to the surveyquestions from the plurality of viewers via the UI. The answers to thesurvey questions of a disclosed example are textual comments. Thesynchronized data of a disclosed example includes the answers to thesurvey questions.

The plurality of viewers of a disclosed example is at a location.

The plurality of viewers of a disclosed example is at a plurality oflocations.

A first set of the plurality of viewers of a disclosed example is at afirst location and a second set of the plurality of viewers are at asecond location different from the first location.

A first set of the plurality of viewers of a disclosed example isviewing the media instance at a first time and a second set of theplurality of viewers are viewing the media instance at a second timedifferent from the first time.

The reaction data of a disclosed example corresponds to electricalactivity in brain tissue of the user.

The reaction data of a disclosed example corresponds to electricalactivity in muscle tissue of the user.

The reaction data of a disclosed example corresponds to electricalactivity in heart tissue of the user.

Examples described herein include a system comprising: a processorcoupled to a database, the database including a media instance andreaction data of a plurality of viewers, the reaction data generated inresponse to viewing of the media instance and including physiologicalresponse data; a first module that aggregates the reaction data from theplurality of viewers; and a second module that provides remote access toat least one of the reaction data and aggregated reaction data, whereinthe remote access enables interactive analysis of at least one of themedia instance, the reaction data, aggregated reaction data, and parsedreaction data.

Examples described herein include a system comprising: a processorcoupled to a database, the database receiving a media instance andreaction data from a plurality of viewers, the reaction data generatedin response to viewing of the media instance and including physiologicalresponse data; a first module aggregating the reaction data from theplurality of viewers; and a second module enabling remote interactiveanalysis and presentation of the media instance and at least one of thereaction data, aggregated reaction data, and parsed reaction data.

Examples described herein include a system comprising: a processorcoupled to a database, the database receiving a media instance andreaction data from a plurality of viewers, the reaction data generatedin response to viewing of the media instance and including physiologicalresponse data; and an interface coupled to the processor, the interfaceenabling remote interactive manipulation of the reaction datasynchronized to corresponding events of the media instance, themanipulation including at least one of dividing, dissecting,aggregating, parsing, and analyzing the reaction data.

Examples described herein include a method comprising: receiving a mediainstance, the media instance including a plurality of media events;receiving reaction data from a plurality of viewers while the pluralityof viewers are viewing the media instance; automatically analyzing thereaction data; and providing remote access to results of the analyzing,and presenting the results, the presenting including presentingactionable insights corresponding to a portion of the media instance viaat least one of a plurality of renderings, wherein the actionableinsights correspond to emotional reactions of the plurality of viewers.

The analyzing of a disclosed example includes applying expert knowledgeof physiological behavior to the reaction data.

The method of a disclosed example comprises generating a first set ofquestions that represent the results.

The analyzing of a disclosed example includes analyzing the reactiondata in the context of the first set of questions.

The method of a disclosed example comprises selecting at least onerendering of the plurality of renderings.

The method of a disclosed example comprises tagging the selectedrendering with at least one question of the first set of questions.

A user of a disclosed example can modify the presenting of the resultsvia the selecting of at least one rendering of the plurality ofrenderings.

The presenting of a disclosed example includes presenting the resultsvia presentation of the first set of questions.

The method of a disclosed example comprises, in response to the userselecting a question of the first set of questions, presenting an answerto the selected question that includes the actionable insight.

The method of a disclosed example comprises selecting a second set ofquestions that represent the results, wherein the second set ofquestions were generated prior to the first set of questions torepresent previous results from analysis of preceding reaction data of apreceding media instance, wherein the preceding reaction data is similarto the reaction data.

The analyzing of a disclosed example includes analyzing the reactiondata in the context of the second set of questions.

The method of a disclosed example comprises selecting at least onerendering of the plurality of renderings.

The method of a disclosed example comprises tagging the selectedrendering with at least one question of the second set of questions.

A user of a disclosed example can modify the presenting of the resultsvia the selecting of at least one rendering of the plurality ofrenderings.

The presenting of a disclosed example includes presenting the resultsvia presentation of the second set of questions.

The method of a disclosed example comprises, in response to the userselecting a question of the second set of questions, presenting ananswer to the selected question that includes the actionable insight.

The method of a disclosed example comprises selecting a set of thereaction data to which the analyzing is applied, the selecting includingselecting a portion of the media instance to which the set of thereaction data corresponds. The portion of a disclosed example includes apoint in time. The portion of a disclosed example includes a period oftime.

The method of a disclosed example comprises generating aggregatedreaction data by aggregating the reaction data from the plurality ofviewers.

The aggregating of a disclosed example comprises aggregating thereaction data according to at least one of maximums, minimums, averages,deviations, derivatives, amplitudes, and trends of at least oneparameter of the reaction data.

The method of a disclosed example comprises generating synchronized databy synchronizing the plurality of media events of the media instancewith the reaction data.

The method of a disclosed example comprises enabling remote interactivemanipulation of the media instance.

The method of a disclosed example comprises enabling remote interactivemanipulation of the reaction data.

The method of a disclosed example comprises enabling remote interactivemanipulation of the plurality of renderings.

The method of a disclosed example comprises enabling remote interactivemanipulation of the actionable insights.

The plurality of renderings of a disclosed example includes text,charts, graphs, histograms, images, and video.

The reaction data of a disclosed example includes at least one ofphysiological responses, survey results, feedback generated by theviewers, metadata, and derived statistics

The reaction data of a disclosed example includes physiologicalresponses

The reaction data of a disclosed example includes survey results.

The reaction data of a disclosed example includes feedback generated bythe viewers.

The reaction data of a disclosed example includes metadata, wherein themetadata is event-based metadata.

The reaction data of a disclosed example includes derived statistics,wherein the derived statistics are derived statistics for indicators ofsuccess and failure of the media instance.

Receiving the reaction data of a disclosed example comprises receivingthe reaction data from a plurality of sensor devices via wirelesscouplings, wherein each viewer wears a sensor device of the plurality ofsensor devices.

The reaction data of a disclosed example corresponds to electricalactivity in brain tissue of the user.

The reaction data of a disclosed example corresponds to electricalactivity in muscle tissue of the user.

The reaction data of a disclosed example corresponds to electricalactivity in heart tissue of the user.

A first set of the plurality of viewers of a disclosed example is at afirst location and a second set of the plurality of viewers is at asecond location different from the first location

A first set of the plurality of viewers of a disclosed example isviewing the media instance at a first time and a second set of theplurality of viewers is viewing the media instance at a second timedifferent from the first time.

Examples described herein include a method comprising: receiving a mediainstance; receiving reaction data from a plurality of viewers while theplurality of viewers are viewing the media instance; automaticallyanalyzing the reaction data; and presenting the results by presentingactionable insights corresponding to a portion of the media instance viaat least one of a plurality of renderings, wherein the actionableinsights correspond to emotional reactions of the plurality of viewers.

Examples described herein include a method comprising: receiving a mediainstance; receiving reaction data from a plurality of viewers viewingthe media instance; analyzing the reaction data; and presenting resultsof the analyzing by presenting a set of questions corresponding to aportion of the media instance, the set of questions corresponding to atleast one of a plurality of renderings, wherein answers to questions ofthe set of questions present actionable insights of the reaction data,the actionable insights corresponding to emotional reactions of theplurality of viewers.

Examples described herein include a system comprising: a processorcoupled to a database, the database including a media instance andreaction data, the media instance including a plurality of media events,the reaction data received from a plurality of viewers while theplurality of viewers are viewing the media instance; a first modulecoupled to the processor, the first module analyzing the reaction data;and a second module coupled to the processor, the second modulecomprising a plurality of renderings and a user interface (UI) thatprovide remote access to results of the analyzing and the results, theresults including actionable insights corresponding to a portion of themedia instance, wherein the actionable insights correspond to emotionalreactions of the plurality of viewers.

The analyzing of a disclosed example includes applying expert knowledgeof physiological behavior to the reaction data.

The first module of a disclosed example generates a first set ofquestions that represent the results.

The analyzing of a disclosed example includes analyzing the reactiondata in the context of the first set of questions.

At least one of the second module and the UI of a disclosed exampleenables selection of at least one rendering of the plurality ofrenderings.

At least one of the second module and the UI of a disclosed exampleenables tagging of a selected rendering with at least one question ofthe first set of questions.

A user of a disclosed example can modify presentation of the results viathe UI by selecting at least one rendering of the plurality ofrenderings.

At least one of the second module and the UI of a disclosed examplepresents the results via presentation of the first set of questions.

In response to receipt of a selected question of the first set ofquestions, the second module of a disclosed example presents an answerto the selected question that includes the actionable insight.

The first module of a disclosed example selects a second set ofquestions that represent the results, wherein the second set ofquestions were generated prior to the first set of questions torepresent previous results from analysis of preceding reaction data of apreceding media instance, wherein the preceding reaction data is similarto the reaction data.

The analyzing of a disclosed example includes analyzing the reactiondata in the context of the second set of questions.

The UI of a disclosed example enables selection of at least onerendering of the plurality of renderings.

The method of a disclosed example comprises tagging the selectedrendering with at least one question of the second set of questions.

A user of a disclosed example can modify presentation of the results viathe UI by the selecting of at least one rendering of the plurality ofrenderings.

At least one of the second module and the UI of a disclosed examplepresents the results via presentation of the second set of questions.

In response to the user selecting a question of the second set ofquestions, at least one of the second module and the UI of a disclosedexample presents an answer to the selected question that includes theactionable insight.

The UI of a disclosed example enables selection of a set of the reactiondata to which the analyzing is applied, the selecting includingselecting a portion of the media instance to which the set of thereaction data corresponds. The portion of a disclosed example includes apoint in time. The portion of a disclosed example includes a period oftime.

The first module of a disclosed example generates aggregated reactiondata by aggregating the reaction data from the plurality of viewers.

The aggregating of a disclosed example comprises aggregating thereaction data according to at least one of maximums, minimums, averages,deviations, derivatives, amplitudes, and trends of at least oneparameter of the reaction data.

The method of a disclosed example comprises generating synchronized databy synchronizing the plurality of media events of the media instancewith the reaction data.

The method of a disclosed example comprises enabling remote interactivemanipulation of the media instance via the UI.

The method of a disclosed example comprises enabling remote interactivemanipulation of the reaction data via the UI.

The method of a disclosed example comprises enabling remote interactivemanipulation of the plurality of renderings via the UI.

The method of a disclosed example comprises enabling remote interactivemanipulation of the actionable insights via the UI.

The plurality of renderings of a disclosed example includes text,charts, graphs, histograms, images, and video.

The reaction data of a disclosed example includes at least one ofphysiological responses, survey results, feedback generated by theviewers, metadata, and derived statistics.

The reaction data of a disclosed example includes physiologicalresponses.

The reaction data of a disclosed example includes survey results.

The reaction data of a disclosed example includes feedback generated bythe viewers.

The reaction data of a disclosed example includes metadata, wherein themetadata is event-based metadata.

The reaction data of a disclosed example includes derived statistics,wherein the derived statistics are derived statistics for indicators ofsuccess and failure of the media instance.

The method of a disclosed example comprises a plurality of sensordevices, wherein each viewer wears a sensor device of the plurality ofsensor devices, wherein each sensor device receives the reaction datafrom a corresponding view and transmits the reaction data to at leastone of the first module and the database.

The reaction data of a disclosed example corresponds to electricalactivity in brain tissue of the user.

The reaction data of a disclosed example corresponds to electricalactivity in muscle tissue of the user.

The reaction data of a disclosed example corresponds to electricalactivity in heart tissue of the user.

A first set of the plurality of viewers of a disclosed example is at afirst location and a second set of the plurality of viewers of adisclosed example is at a second location different from the firstlocation.

A first set of the plurality of viewers of a disclosed example isviewing the media instance at a first time and a second set of theplurality of viewers is viewing the media instance at a second timedifferent from the first time.

Examples described herein include a system comprising: a processorcoupled to a database, the database receiving a media instance andreaction data from a plurality of viewers while the plurality of viewersare viewing the media instance; a first module coupled to the processor,the first module automatically analyzing the reaction data; and a secondmodule coupled to the processor, the second module presenting theresults by presenting actionable insights corresponding to a portion ofthe media instance via at least one of a plurality of renderings,wherein the actionable insights correspond to emotional reactions of theplurality of viewers.

Examples described herein include a system comprising: a processorcoupled to a database, the database receiving a media instance andreaction data from a plurality of viewers viewing the media instance; afirst module coupled to the processor, the first module analyzing thereaction data; and a second module coupled to the processor, the secondmodule presenting results of the analyzing by presenting a set ofquestions corresponding to a portion of the media instance, the set ofquestions corresponding to at least one of a plurality of renderings,wherein answers to questions of the set of questions present actionableinsights of the reaction data, the actionable insights corresponding toemotional reactions of the plurality of viewers.

Examples described herein may be implemented using a conventionalgeneral purpose or a specialized digital computer or microprocessor(s)programmed according to the teachings of the present disclosure, as willbe apparent to those skilled in the computer art Appropriate softwarecoding can readily be prepared by skilled programmers based on theteachings of the present disclosure, as will be apparent to thoseskilled in the software art. The teachings of this disclosure may alsobe implemented by the preparation of integrated circuits or byinterconnecting an appropriate network of conventional componentcircuits, as will be readily apparent to those skilled in the art.

A disclosed example includes a computer program product which is amachine readable medium (media) having instructions stored thereon/inwhich can be used to program one or more computing devices to performany of the features presented herein. The machine readable medium caninclude, but is not limited to, one or more types of disks includingfloppy disks, optical discs, DVD, CD-ROMs, micro drive, andmagneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flashmemory devices, magnetic or optical cards, nanosystems (includingmolecular memory ICs), or any type of media or device suitable forstoring instructions and/or data. Stored on any one of the computerreadable medium (media), the teachings of the present disclosure includesoftware for controlling both the hardware of the generalpurpose/specialized computer or microprocessor, and for enabling thecomputer or microprocessor to interact with a human viewer or othermechanism utilizing the results of the teachings of this disclosure.Such software may include, but is not limited to, device drivers,operating systems, execution environments/containers, and applications.

The examples described herein include and/or run under and/or inassociation with a processing system. The processing system includes anycollection of processor-based devices or computing devices operatingtogether, or components of processing systems or devices, as is known inthe art. For example, the processing system can include one or more of aportable computer, portable communication device operating in acommunication network, and/or a network server. The portable computercan be any of a number and/or combination of devices selected from amongpersonal computers, cellular telephones, personal digital assistants,portable computing devices, and portable communication devices, but isnot so limited. The processing system can include components within alarger computer system.

The processing system of a disclosed example includes at least oneprocessor and at least one memory device or subsystem. The processingsystem can also include or be coupled to at least one database. The term“processor” as generally used herein refers to any logic processingunit, such as one or more central processing units (CPUs), digitalsignal processors (DSPs), application-specific integrated circuits(ASIC), etc. The processor and memory can be monolithically integratedonto a single chip, distributed among a number of chips or components ofthe systems described herein, and/or provided by some combination ofalgorithms. The methods described herein can be implemented in one ormore of software algorithm(s), programs, firmware, hardware, components,circuitry, in any combination.

The components described herein can be located together or in separatelocations. Communication paths couple the components and include anymedium for communicating or transferring files among the components. Thecommunication paths include wireless connections, wired connections, andhybrid wireless/wired connections. The communication paths also includecouplings or connections to networks including local area networks(LANs), metropolitan area networks (MANs), wide area networks (WANs),proprietary networks, interoffice or backend networks, and the Internet.Furthermore, the communication paths include removable fixed mediumslike floppy disks, hard disk drives, and CD-ROM disks, as well as flashRAM, Universal Serial Bus (USB) connections, RS-232 connections,telephone lines, buses, and electronic mail messages.

Aspects of the systems and methods described herein may be implementedas functionality programmed into any of a variety of circuitry,including programmable logic devices (PLDs), such as field programmablegate arrays (FPGAs), programmable array logic (PAL) devices,electrically programmable logic and memory devices and standardcell-based devices, as well as application specific integrated circuits(ASICs). Some other possibilities for implementing aspects of thesystems and methods include: microcontrollers with memory (such aselectronically erasable programmable read only memory (EEPROM)),embedded microprocessors, firmware, software, etc. Furthermore, aspectsof the systems and methods may be embodied in microprocessors havingsoftware-based circuit emulation, discrete logic (sequential andcombinatorial), custom devices, fuzzy (neural) logic, quantum devices,and hybrids of any of the above device types. Of course the underlyingdevice technologies may be provided in a variety of component types,e.g., metal-oxide semiconductor field-effect transistor (MOSFET)technologies like complementary metal-oxide semiconductor (CMOS),bipolar technologies like emitter-coupled logic (ECL), polymertechnologies (e.g., silicon-conjugated polymer and metal-conjugatedpolymer-metal structures), mixed analog and digital, etc.

It should be noted that any system, method, and/or other componentsdisclosed herein may be described using computer aided design tools andexpressed (or represented), as data and/or instructions embodied invarious computer-readable media, in terms of their behavioral, registertransfer, logic component, transistor, layout geometries, and/or othercharacteristics. Computer-readable media in which such formatted dataand/or instructions may be embodied include, but are not limited to,non-volatile storage media in various forms (e.g., optical, magnetic orsemiconductor storage media) and carrier waves that may be used totransfer such formatted data and/or instructions through wireless,optical, or wired signaling media or any combination thereof. Examplesof transfers of such formatted data and/or instructions by carrier wavesinclude, but are not limited to, transfers (uploads, downloads, e-mail,etc.) over the Internet and/or other computer networks via one or moredata transfer protocols (e.g., HTTP, HTTPs, FTP, SMTP, WAP, etc.). Whenreceived within a computer system via one or more computer-readablemedia, such data and/or instruction-based expressions of the abovedescribed components may be processed by a processing entity (e.g., oneor more processors) within the computer system in conjunction withexecution of one or more other computer programs.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense as opposed to anexclusive or exhaustive sense; that is to say, in a sense of “including,but not limited to.” Words using the singular or plural number alsoinclude the plural or singular number respectively. Additionally, thewords “herein,” “hereunder,” “above,” “below,” and words of similarimport, when used in this application, refer to this application as awhole and not to any particular portions of this application. When theword “or” is used in reference to a list of two or more items, that wordcovers all of the following interpretations of the word: any of theitems in the list, all of the items in the list and any combination ofthe items in the list.

The above description of example systems and methods is not intended tobe exhaustive or to limit the systems and methods to the precise formsdisclosed. While specific examples of, and examples for, the systems andmethods are described herein for illustrative purposes, variousequivalent modifications are possible within the scope of the systemsand methods, as those skilled in the relevant art will recognize. Theteachings of the systems and methods provided herein can be applied toother systems and methods, not only for the systems and methodsdescribed above.

The elements and acts of the various examples described above can becombined to provide other examples. These and other changes can be madeto the systems and methods in light of the above detailed description.

In general, in the following claims, the terms used should not beconstrued to limit the claims to the specific examples disclosed in thespecification and the claims, but should be construed to include allsystems and methods under the claims. Accordingly, the examples are notlimited by the disclosure, but instead the scope of the examples is tobe determined entirely by the claims.

While certain aspects of the examples are presented below in certainclaim forms, the inventors contemplate the various aspects of theexamples in any number of claim forms. Accordingly, the inventorsreserve the right to add additional claims after filing the applicationto pursue such additional claim forms for other aspects disclosed in thevarious examples.

What is claimed is:
 1. A system comprising: a synchronizer to: timeshift first neuro-response data gathered from an audience member exposedto media a first amount to align the first neuro-response data withsecond neuro-response data simultaneously gathered from the audiencemember to form aligned data, the first neuro-response data representinga first response to a first sensory component of the media and thesecond neuro-response data representing a second response to a secondsensory component of the media, the first sensory component of the mediato be observed by a first sense of the audience member and the secondsensory component to be observed by a second sense of the audiencemember different from the first sense; and time shift the secondneuro-response data a second amount, the first amount different from thesecond amount, the first amount based on a first cognitive delay of abrain of the audience member associated with the first neuro-responsedata and the second amount based on a second cognitive delay of thebrain of the audience member associated with the second neuro-responsedata; and an analyzer to: determine an effectiveness of the media basedon the aligned data; and output the effectiveness for presentation viadisplay device.
 2. The system of claim 1, wherein the audience member isa first audience member and the aligned data is first aligned data, thesynchronizer to: time shift third neuro-response data gathered from asecond audience member exposed to the media a third amount to align thethird neuro-response data with fourth neuro-response data simultaneouslygathered from the second audience member to form second aligned data,the third neuro-response data associated with the first sensorycomponent of the media and the fourth neuro-response data associatedwith the second sensory component of the media; and aggregate the firstaligned data and the second aligned data to form first aggregated data,the analyzer to determine the effectiveness of the media based on thefirst aggregated data.
 3. The system of claim 2, wherein the analyzer isto determine the effectiveness based on amplitude change in the firstaggregated data.
 4. The system of claim 2, wherein the synchronizer isto: aggregate the first neuro-response data and the third neuro-responsedata to form second aggregated data; aggregate the second neuro-responsedata and the fourth neuro-response data to form third aggregated data;and synchronize a display of the one or more of the first aggregateddata, the second aggregated data, the third aggregated data, or theeffectiveness with a display of the media, the analyzer to output thesynchronized display for presentation via a display device.
 5. Thesystem of claim 1, wherein the aligned data is first aligned data andthe synchronizer is to time shift third neuro-response data gatheredfrom the audience member to align the third neuro-response data withfourth neuro-response data to form second aligned data, the thirdneuro-response data and the fourth neuro-response data simultaneouslygathered from the audience member at a point in time subsequent to thegathering of the first neuro-response data and second neuro-responsedata, the third neuro-response data representing a third response to thefirst sensory component of the media and the fourth neuro-response datarepresenting a fourth response to the second sensory component of themedia.
 6. The system of claim 5, wherein the analyzer is to determinethe effectiveness of the media based on a comparison of the firstaligned data and the second aligned data.
 7. The system of claim 1,wherein the first sensory component of the media includes a visualcomponent and the second sensory component of the media include anauditory component.
 8. A method comprising: time shifting firstneuro-response data gathered from an audience member exposed to media afirst amount to align the first neuro-response data with secondneuro-response data simultaneously gathered from the audience member toform aligned data, the first neuro-response data representing a firstresponse to a first sensory component of the media and the secondneuro-response data representing a second response to a second sensorycomponent of the media, the first sensory component of the media to beobserved by a first sense of the audience member and the second sensorycomponent to be observed by a second sense of the audience memberdifferent from the first sense; time shifting the second neuro-responsedata a second amount, the first amount different from the second amount,the first amount based on a first cognitive delay of a brain of theaudience member associated with the first neuro-response data and thesecond amount based on a second cognitive delay of the brain of theaudience member associated with the second neuro-response data;determining an effectiveness of the media based on the aligned data; andoutputting the effectiveness for presentation via display device.
 9. Themethod of claim 8, wherein the audience member is a first audiencemember and the aligned data is first aligned data, and furtherincluding: time shifting third neuro-response data gathered from asecond audience member exposed to the media a third amount to align thethird neuro-response data with fourth neuro-response data simultaneouslygathered from the second audience member to form second aligned data,the third neuro-response data associated with the first sensorycomponent of the media and the fourth neuro-response data associatedwith the second sensory component of the media; aggregating the firstaligned data and the second aligned data to form first aggregated data;and determining the effectiveness of the media based on the firstaggregated data.
 10. The method of claim 9, further includingdetermining the effectiveness based on amplitude change in the firstaggregated data.
 11. The method of claim 9, further including:aggregating the first neuro-response data and the third neuro-responsedata to form second aggregated data; aggregating the secondneuro-response data and the fourth neuro-response data to form thirdaggregated data; synchronizing a display of the one or more of the firstaggregated data, the second aggregated data, the third aggregated data,or the effectiveness with a display of the media; and outputting thesynchronized display for presentation via a display device.
 12. Themethod of claim 8, wherein the aligned data is first aligned data andfurther including time shifting third neuro-response data gathered fromthe audience member to align the third neuro-response data with fourthneuro-response data to form second aligned data, the thirdneuro-response data and the fourth neuro-response data simultaneouslygathered from the audience member at a point in time subsequent to thegathering of the first neuro-response data and second neuro-responsedata, the third neuro-response data representing a third response to thefirst sensory component of the media and the fourth neuro-response datarepresenting a fourth response to the second sensory component of themedia.
 13. The method of claim 12, further including determining theeffectiveness of the media based on a comparison of the first aligneddata and the second aligned data.
 14. The method of claim 8, wherein thefirst sensory component of the media includes a visual component and thesecond sensory component of the media include an auditory component 15.A tangible machine readable storage device or storage disc comprisinginstructions which, when executed by a machine, cause the machine to atleast time shift first neuro-response data gathered from an audiencemember exposed to media a first amount to align the first neuro-responsedata with second neuro-response data simultaneously gathered from theaudience member to form aligned data, the first neuro-response datarepresenting a first response to a first sensory component of the mediaand the second neuro-response data representing a second response to asecond sensory component of the media, the first sensory component ofthe media to be observed by a first sense of the audience member and thesecond sensory component to be observed by a second sense of theaudience member different from the first sense; time shift the secondneuro-response data a second amount, the first amount different from thesecond amount, the first amount based on a first cognitive delay of abrain of the audience member associated with the first neuro-responsedata and the second amount based on a second cognitive delay of thebrain of the audience member associated with the second neuro-responsedata; determine an effectiveness of the media based on the aligned data;and output the effectiveness for presentation via display device. 16.The storage device or storage disc of claim 15, wherein the audiencemember is a first audience member and the aligned data is first aligneddata, and the instructions further cause the machine to: time shiftthird neuro-response data gathered from a second audience member exposedto the media a third amount to align the third neuro-response data withfourth neuro-response data simultaneously gathered from the secondaudience member to form second aligned data, the third neuro-responsedata associated with the first sensory component of the media and thefourth neuro-response data associated with the second sensory componentof the media; aggregate the first aligned data and the second aligneddata to form first aggregated data; and determine the effectiveness ofthe media based on the first aggregated data.
 17. The storage device orstorage disc of claim 16, wherein the instructions further cause themachine to determine the effectiveness based on amplitude change in thefirst aggregated data.
 18. The storage device or storage disc of claim16, wherein the instructions further cause the machine to: aggregate thefirst neuro-response data and the third neuro-response data to formsecond aggregated data; aggregate the second neuro-response data and thefourth neuro-response data to form third aggregated data; synchronize adisplay of the one or more of the first aggregated data, the secondaggregated data, the third aggregated data, or the effectiveness with adisplay of the media; and output the synchronized display forpresentation via a display device.
 19. The storage device or storagedisc of claim 15, wherein the aligned data is first aligned data and theinstructions further cause the machine to time shift thirdneuro-response data gathered from the audience member to align the thirdneuro-response data with fourth neuro-response data to form secondaligned data, the third neuro-response data and the fourthneuro-response data simultaneously gathered from the audience member ata point in time subsequent to the gathering of the first neuro-responsedata and second neuro-response data, the third neuro-response datarepresenting a third response to the first sensory component of themedia and the fourth neuro-response data representing a fourth responseto the second sensory component of the media.
 20. The storage device orstorage disc of claim 19, wherein the instructions further cause themachine to determine the effectiveness of the media based on acomparison of the first aligned data and the second aligned data.